Electricity market: Demand Response and price optimization

One of the main research objectives in Demand Response (DR) is the desig=
n and implementation of technologies and mechanisms to lower the electricit=
y consumption via energy efficiency measures, and to improve the electricit=
y consumption via demand shifting. Increasing energy efficiency requires a =
reduction of energy demand peaks by shifting part of the energy consumption=
in off-peak hours. This can be done via DR mechanisms and load control.

Demand shifting can provide a number of advantages to the energy system =
[1]:

Load management can improve system security by allowing a demand reduct=
ion in emergency situations.

In periods of peak loads even a limited reduction in demand can lead to=
significant reductions in electricity prices on the market.

If users receive information about prices, energy consumption becomes m=
ore closely related to the energy cost, thus increasing market efficiency: =
the demand is moved from periods of high load (typically associated with hi=
gh prices) to periods of low load.

Load management can limit the need for expensive and polluting power ge=
nerators, leading to better environmental conditions.

Potential benefits and implementation schemes for DR mechanisms are well=
documented in literature. DR programs can be defined as methods to induce =
deviations from the usual consumption pattern in response to stimuli, such =
as dynamic prices, incentives for load reductions, tax exemptions, or subsi=
dies. They can be divided in two main groups: price-based and incentive-bas=
ed mechanisms [2], [3] and [4].

Price-based demand response is related to the changes in energy consump=
tion by customers in response to the variations in their purchase prices. T=
his group includes DR mechanisms like Time-of-Use (ToU) pricing, Real Time =
Pricing (RTP) and Critical-Peak Pricing (CPP) rates. If the price varies si=
gnificantly, customers can respond to the price structure with changes in t=
heir pattern of energy use. They can reduce their energy costs by adjusting=
the time of the energy usage by increasing consumption in periods of lower=
prices and reducing consumption when prices are higher. ToU mechanisms def=
ine different prices for electricity usage during different periods: the ta=
riffs reflect the average cost of generating and delivering power during th=
ose periods. For RTP the price of electricity is defined for shorter period=
s of time, usually 1 h, again reflecting the changes in the wholesale price=
of electricity. In RTP customers usually have the information about prices=
. CPP is a hybrid ToU RTP program. This mechanisms is based on the real tim=
e cost of energy in peak price periods, and has various methods of implemen=
tation.

Incentive-based demand response consists in programs with fixed or time=
varying incentives for customers in addition to their electricity tariffs.=
Incentive-Based programs (IB) include Direct Load Control (DLC), Interrupt=
ible/Curtailable service (I/C), Emergency Demand Response program (EDR), Ca=
pacity market Program (CAP), Demand Bidding (DB) and Ancillary Service (A/S=
) programs. Classical IB programs include DLC and I/C programs. Market-Base=
d IB programs include EDR, DB, CAP, and the A/S programs. In classical IBP,=
customers receive participation payments (e.g. discount rate) for their pa=
rticipation in the programs. In Market-Based programs, participants receive=
money for the amount of their load reduction during critical conditions. I=
n I/C programs, participants are asked to reduce their load to fixed values=
and participants who do not respond can pay penalties based on the program=
conditions. DB are programs in which consumers are encouraged to change th=
eir energy consumption pattern and decline their peak load in return for fi=
nancial rewards and to avoid penalties. In EDR programs, customers are paid=
incentives for load reductions duringemergency conditions.

Demand Response mechanisms and load control in the electricity market re=
present an important area of research at international level, and the marke=
t liberalization is opening new perspectives. This calls for the developmen=
t of methodologies and tools that energy providers can use to define specif=
ic business models and pricing schemes.

Every actor in the electricity market has different objectives. For exam=
ple, retailers and generators aim to maximize their own profit by reducing =
their costs. In contrast, customers would like their electricity bills as l=
ow as possible[5]. Game theoretical methods can also be used to capture the=
conflicting economic interests of the retailer and their consumers. Author=
s in [15] propose optimization models for the maximization of the expected =
market profits for the retailer and the minimization of the electricity cos=
t for the consumer.

One implementation approach of DR mechanisms in the electricity market c=
onsists in defining economically and environmentally sustainable energy pri=
cing schemes. In this field, optimization approaches to define dynamic pric=
es have been proposed, and they focus on the definition of day-ahead prices=
for a period of 24 hours and for a single customer (or a single group of h=
omogeneous customers). In [10], the response of a non-linear mathematical m=
odel is analyzed for the calculation of the optimal prices for electricity =
assuming default customers under different scenarios over a 24h period. [10=
] defines a model of an electric energy service provider in the environment=
of the deregulated electricity market. This problem studies the impact on =
the profits of several factors, such as the price strategy, the discount on=
tariffs and the elasticity of customer demand functions always over a 24h =
period.

Consumers may decide to modify their load profile to reduce their electr=
icity costs. For this reason, it is important to analyze the effect that th=
e market structure has on the elasticity demand for electricity. [6] propos=
es an elastic model to characterize the demand-response behavior and load m=
anagement with ToU programs and it describes how the consumers behavior can=
be modeled using a matrix of self and cross-elasticities. [7] and [8] take=
into account also other schemes, and rely on the elastic model proposed in=
[6] to model the demand-response behavior. [9] assesses the impacts of ToU=
tariffs on a dataset of residential users in terms of changes in electrici=
ty demand, price savings, peak load shifting and peak electricity demand at=
sub-station level.

Response of the customers to the DR programs affects the daily load curv=
e. Therefore, the Load Duration Curve (LDC) changes due to the responsivene=
ss of the customers over a year and even the participation of the customers=
in DR programs can have considerable effects on the LDC [11]: the effects =
of DR need to be investigated over the daily time horizon. [1] has adapted =
elasticity model mentioned above to ToU based prices and considered scenari=
os over a 24h period to better identify trends and assess how the character=
istics of the market and the customers affect the consumption annual profil=
es.

Consumption and cost awareness has an important role for the effectivene=
ss of demand response schemes for pricing optimization. [12] describes a sy=
stem architecture for monitoring the electricity consumption and displaying=
consumption profiles to increase awareness. [13, 14] study how customers r=
espond to price changes, and which price indicators are more relevant on th=
is respect.